A large volume of information data is generated with the rapid development of Internet and information industry. Operation and maintenance personnel need to purchase storage devices in time for effectively storing the ever increasing data. However, due to inaccurate purchase budget, equipment is often left unused as a result of excessive purchase, and manpower waste and financial loss are brought to companies and enterprises. Therefore, those skilled in the art actively pursue data analysis and data visualization research projects, to analyze the basic change trend through historical data in a capacity time sequence, in order to accurately forecast the future capacity trend, achieve the goal of intelligently customizing a purchase plan, reduce the purchase cost for a company and avoid wastes.
In the prior art, the methods for forecasting the future capacity trend through the historical data in the capacity time sequence have the following shortcomings: (1) with regard to the human observation method, although the professionals with substantial experience can relatively accurately judge the trend of the capacity time sequence data, the forecasting error is still relatively excessive; and (2) the threshold alarm method can remind relevant operation and maintenance personnel in time of purchasing, but cannot give a reasonable suggestion for the purchase amount, thus easily leading to excessive capacity expansion and waste.